tidyterra

The goal of tidyterra is to provide methods from tidyverse packages for SpatRaster and SpatVector objects created with terra. It also provides ggplot2 geoms and scales for plotting those objects.

Please cite tidyterra as:

Hernangómez, D. (2023). Using the tidyverse with terra objects: the tidyterra package. Journal of Open Source Software, 8(91), 5751, https://doi.org/10.21105/joss.05751.

A BibTeX entry for LaTeX users is:

@article{Hernangómez2023,
  doi = {10.21105/joss.05751},
  url = {https://doi.org/10.21105/joss.05751},
  year = {2023},
  publisher = {The Open Journal},
  volume = {8},
  number = {91},
  pages = {5751},
  author = {Diego Hernangómez},
  title = {Using the {tidyverse} with {terra} objects: the {tidyterra} package},
  journal = {Journal of Open Source Software}
}

Overview

The full manual for the latest release of tidyterra on CRAN is online: https://dieghernan.github.io/tidyterra/

Methods implemented in tidyterra work differently depending on the type of Spat* object:

• SpatVector: Methods are implemented using terra::as.data.frame() coercion. Rows correspond to geometries and columns correspond to attributes of each geometry.

• SpatRaster: Methods can be applied to layers or cells. tidyterra’s overall approach is to treat the layers as columns of a tibble and the cells as rows. For example, select(a_spatraster, 1) selects the first layer of a SpatRaster object.

Implemented methods return the same type of object as the input, unless the method is expected to return another type of object. For example, as_tibble() returns a tibble.

Current methods and functions provided by tidyterra are:

[!IMPORTANT]

A note on performance

tidyterra is a user-friendly wrapper around terra that provides tidyverse-style methods and verbs. This approach has a performance cost.

If you frequently use terra or work with large SpatRaster objects, terra is usually much faster. Whenever possible, each tidyterra function refers to its equivalent in terra.

As a rule of thumb, if your raster has fewer than 10,000,000 data slots, for example terra::ncell(your_rast) * terra::nlyr(your_rast) < 1e7, tidyterra is a good fit.

When plotting rasters, resampling is performed automatically (as terra::plot() does, see the help page). You can adjust this with the maxcell argument.

Installation

Install tidyterra from CRAN:

install.packages("tidyterra")

Check the documentation for the development version at https://dieghernan.github.io/tidyterra/dev/

You can install the development version of tidyterra with:

# install.packages("pak")
pak::pak("dieghernan/tidyterra")

Alternatively, you can install tidyterra using r-universe:

# Enable this universe
install.packages(
  "tidyterra",
  repos = c(
    "https://dieghernan.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

Examples

SpatRaster objects

This basic example shows how to manipulate and plot SpatRaster objects:

library(tidyterra)
library(terra)

# Temperatures
rastertemp <- rast(system.file("extdata/cyl_temp.tif", package = "tidyterra"))

rastertemp
#> class       : SpatRaster
#> size        : 87, 118, 3  (nrow, ncol, nlyr)
#> resolution  : 3881.255, 3881.255  (x, y)
#> extent      : -612335.4, -154347.3, 4283018, 4620687  (xmin, xmax, ymin, ymax)
#> coord. ref. : World_Robinson (ESRI:54030)
#> source      : cyl_temp.tif
#> names       :   tavg_04,   tavg_05,   tavg_06
#> min values  :  1.885463,  5.817587, 10.463377
#> max values  : 13.283829, 16.740898, 21.113781

# Rename
rastertemp <- rastertemp |>
  rename(April = tavg_04, May = tavg_05, June = tavg_06)

# Facet all layers
library(ggplot2)

ggplot() +
  geom_spatraster(data = rastertemp) +
  facet_wrap(~lyr, ncol = 2) +
  scale_fill_whitebox_c(
    palette = "muted",
    labels = scales::label_number(suffix = "º"),
    n.breaks = 12,
    guide = guide_legend(reverse = TRUE)
  ) +
  labs(
    fill = "",
    title = "Average temperature in Castile and Leon (Spain)",
    subtitle = "Months of April, May and June"
  )

# Create the difference between two months.
variation <- rastertemp |>
  mutate(diff = June - May) |>
  select(variation = diff)

# Add a SpatVector overlay.
prov <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

ggplot(prov) +
  geom_spatraster(data = variation) +
  geom_spatvector(fill = NA) +
  scale_fill_whitebox_c(
    palette = "deep",
    direction = -1,
    labels = scales::label_number(suffix = "º"),
    n.breaks = 5
  ) +
  theme_minimal() +
  coord_sf(crs = 25830) +
  labs(
    fill = "Variation",
    title = "Temperature variation in Castile and Leon (Spain)",
    subtitle = "Average temperatures: June vs. May"
  )

tidyterra also provides a geom for plotting RGB SpatRaster objects, such as map tiles, with ggplot2:

rgb_tile <- rast(system.file("extdata/cyl_tile.tif", package = "tidyterra"))

ggplot(prov) +
  geom_spatraster_rgb(data = rgb_tile) +
  geom_spatvector(fill = NA) +
  theme_light() +
  # Change the CRS and datum (useful for relabeling graticules).
  coord_sf(crs = 3857, datum = 3857)

tidyterra provides ggplot2 scales for plotting maps with hypsometric tints:

asia <- rast(system.file("extdata/asia.tif", package = "tidyterra"))

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_hypso_tint_c(
    palette = "gmt_globe",
    labels = scales::label_number(),
    # Further refinements
    breaks = c(-10000, -5000, 0, 2000, 5000, 8000),
    guide = guide_colorbar(reverse = TRUE)
  ) +
  labs(
    fill = "elevation (m)",
    title = "Hypsometric map of Asia"
  ) +
  theme(
    legend.position = "bottom",
    legend.title.position = "top",
    legend.key.width = rel(3),
    legend.ticks = element_line(colour = "black", linewidth = 0.3),
    legend.direction = "horizontal"
  )

SpatVector objects

This basic example shows how to manipulate and plot SpatVector objects:

vect(system.file("ex/lux.shp", package = "terra")) |>
  mutate(pop_dens = POP / AREA) |>
  glimpse() |>
  autoplot(aes(fill = pop_dens)) +
  scale_fill_whitebox_c(palette = "pi_y_g") +
  labs(
    fill = "population per km2",
    title = "Population density of Luxembourg",
    subtitle = "By canton"
  )
#> #  A SpatVector 12 x 7
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y): ([5° 44' 38.9" E / 6° 31' 41.71" E] , [49° 26' 52.11" N / 50° 10' 53.84" N])
#> 
#> $ ID_1     <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3
#> $ NAME_1   <chr> "Diekirch", "Diekirch", "Diekirch", "Diekirch", "Diekirch", "…
#> $ ID_2     <dbl> 1, 2, 3, 4, 5, 6, 7, 12, 8, 9, 10, 11
#> $ NAME_2   <chr> "Clervaux", "Diekirch", "Redange", "Vianden", "Wiltz", "Echte…
#> $ AREA     <dbl> 312, 218, 259, 76, 263, 188, 129, 210, 185, 251, 237, 233
#> $ POP      <dbl> 18081, 32543, 18664, 5163, 16735, 18899, 22366, 29828, 48187,…
#> $ pop_dens <dbl> 57.95192, 149.27982, 72.06178, 67.93421, 63.63118, 100.52660,…

Feedback

Please leave your feedback or open an issue on https://github.com/dieghernan/tidyterra/issues.

Need help?

Check the FAQs or open a new issue.

You can also ask on Stack Overflow using the tag [tidyterra].

Acknowledgments

tidyterra’s ggplot2 geoms are based on the ggspatial implementation by Dewey Dunnington and ggspatial contributors.